System, method and computer program product for classification within inspection images

ABSTRACT

In accordance with an aspect of the presently disclosed subject matter, there is provided an analysis system for classifying possible defects identified within an inspection image of an inspected object, the system comprising a pattern matcher configured to determine an anchor location with respect to the inspection image, based on a matching of a template and a portion of the inspection image; wherein an accuracy of the determining of the anchor location exceeds a resolution of the inspection image; a distribution analysis module configured to determine, based on the anchor location and a mask which defines different segments within an area, a distribution of a potential defect with respect to one or more of the segments; and a classifier, configured to classify the potential defect based on the distribution.

FIELD OF THE INVENTION

This invention relates to systems and methods for classification withininspection images. Specifically, the invention may be implemented in thefield of manufacturing of artifacts having minute details, such aswafers, photomasks, and electronic circuits.

BACKGROUND OF THE INVENTION

In many implementations, inspected objects are imaged and are searchedthereafter to discover target patterns. For example, aerial images maybe searched for enemy tanks, textile fabric imaged during manufacturemay be searched for holes, and electronic circuits, such as wafers, maybe imaged and searched for defects.

Taking the case of defects searched during manufacture of wafers as anexample, it is clear that while defects can damage the properoperability of the electronic circuit, impact of different defects onthe operation of the electronic circuit may vary. Therefore, somedefects may be of no substantial interest to the inspecting party, e.g.if their impact on circuit operation is low. Furthermore, knowledgeregarding the different defects may be useful for manufacturing offuture similar electronic circuits.

FIGS. 1A and 1B illustrate two types of defects in an electroniccircuit, wherein each of FIGS. 1A and 1B illustrates an electroniccircuit (such as a wafer), scanned using electron beam inspection. Thegrey level in each one of the circuits illustrated in FIGS. 1A and 1B isindicative of the pattern of the electronic circuit in that part of thewafer. For example, materials of different conductivity (such as aconducting material and an isolating material) may have differentreflection indexes which may be translated to different grey levels. Thefollowing discussion pertains to an example in which substantiallydifferent grey level values in the image are indicative of differentmaterials of substantially different electrical conductivity.

Defects 10 a and 10 c (also denoted “edge roughness” defects) arelocated on the boundary of an area of the imaged layer of the waferbetween two different materials. Therefore, the electronic effect ofsuch a defect is relatively limited, and under some circumstances such adefect may be of little interest.

Defects 10 b and 10 d, on the other hand (also denoted “short gate”defects), are located between two areas of the imaged layer of similarmaterials, and may indicate a conductive connection between two parts ofthe electronic circuits which ought to be isolated from each other.Since the electronic effects of such a defect may be relativelysignificant, under some circumstances such a defect may be furtherinspected—e.g. in a higher inspection resolution and/or using slower andmore in-depth image analysis.

SUMMARY OF THE INVENTION

In accordance with an aspect of the presently disclosed subject matter,there is provided an analysis system for classifying possible defectsidentified within an inspection image of an inspected object, the systemcomprising a pattern matcher configured to determine an anchor locationwith respect to the inspection image, based on a matching of a templateand a portion of the inspection image; wherein an accuracy of thedetermining of the anchor location exceeds a resolution of theinspection image; a distribution analysis module configured todetermine, based on the anchor location and a mask which definesdifferent segments within an area, a distribution of a potential defectwith respect to one or more of the segments; and a classifier,configured to classify the potential defect based on the distribution.

In accordance with an embodiment of the presently disclosed subjectmatter, there is further provided a system, wherein the inspected objectis selected from a group consisting of an electronic circuit, a wafer,and a photomask.

In accordance with an embodiment of the presently disclosed subjectmatter, there is further provided a system, wherein the distributionanalysis module is configured to determine the distribution at anaccuracy which exceeds the resolution of the inspection image.

In accordance with an embodiment of the presently disclosed subjectmatter, there is further provided a system, wherein the differentsegments correspond to parts of the inspected object having differentphysical characteristics.

In accordance with an embodiment of the presently disclosed subjectmatter, there is yet further provided a system, wherein the classifieris configured to classify the potential defect according to aclassification in which classes correspond to defect types whoseimplications on an operability of the inspected object differ.

In accordance with an embodiment of the presently disclosed subjectmatter, there is yet further provided a system, wherein the patternmatcher is configured to determine multiple anchor locations withrespect to the inspection image, based on matching of the template andmultiple portions of the inspection image; wherein accuracies of thedetermining of the multiple anchor locations exceed the resolution ofthe inspection image; wherein the distribution analysis module isconfigured to determine distributions of the potential defect withrespect to at least one segment of the mask based on the mask and on themultiple anchor locations; wherein the classifier is configured toclassify the potential defect based on multiple distributions determinedfor the potential defect by the distribution analysis module.

In accordance with an embodiment of the presently disclosed subjectmatter, there is yet further provided a system, wherein the classifieris further configured to select for further scanning potential defectswhich are classified into certain classes, wherein the selectingincludes refraining from selecting potential defects classified into atleast one class other than the certain classes; wherein the systemfurther comprises an inspection module, configured to selectively scan,in a resolution which is higher than the resolution of the inspectionimage, at least one area of the inspected object which is selected basedon locations of selected potential defects.

In accordance with an embodiment of the presently disclosed subjectmatter, there is further provided a system, further comprising areference data generator configured to define the mask based on areference image of an inspected-object reference area, to downsample apart of the reference image, and to generate the template based on aresult of the downsampling.

In accordance with an aspect of the presently disclosed subject matter,there is further provided a computerized method for classifying apotential defect identified within an inspection image of an inspectedobject, the method comprising determining an anchor location withrespect to the inspection image, based on a matching of a template and aportion of the inspection image, wherein an accuracy of the determiningof the anchor location exceeds a resolution of the inspection image;based on a mask which defines different segments within an area and onthe anchor location, determining a distribution of the potential defectwith respect to one or more of the segments; and classifying thepotential defect based on the distribution.

In accordance with an embodiment of the presently disclosed subjectmatter, there is yet further provided a method, wherein the inspectedobject is selected from a group consisting of an electronic circuit, awafer, and a photomask.

In accordance with an embodiment of the presently disclosed subjectmatter, there is yet further provided a method, wherein the determiningof the distribution comprises determining the distribution at anaccuracy which exceeds the resolution of the inspection image.

In accordance with an embodiment of the presently disclosed subjectmatter, there is yet further provided a method, wherein the differentsegments correspond to parts of the inspected object having differentphysical characteristics.

In accordance with an embodiment of the presently disclosed subjectmatter, there is yet further provided a method, wherein the classifyingcomprises classifying the potential defect according to a classificationin which classes correspond to defect types whose implications on anoperability of the inspected object differ.

In accordance with an embodiment of the presently disclosed subjectmatter, there is yet further provided a method, comprising determiningmultiple anchor locations with respect to the inspection image, based onmatching of the template and multiple portions of the inspection image;wherein accuracies of the determining of the multiple anchor locationsexceed the resolution of the inspection image; based on the mask and onthe multiple anchor locations, determining distributions of thepotential defect with respect to one or more of the segments; andclassifying the potential defect based on multiple distributionsdetermined for it.

In accordance with an embodiment of the presently disclosed subjectmatter, there is yet further provided a method, further comprisingselecting for further scanning potential defects which are classifiedinto certain classes, wherein the selecting includes refraining fromselecting potential defects classified into at least one class otherthan the certain classes; and selectively scanning, in a resolutionwhich is higher than the resolution of the inspection image, at leastone area of the inspected object which is selected based on locations ofthe potential defects which are selected.

In accordance with an embodiment of the presently disclosed subjectmatter, there is yet further provided a method, wherein the mask isdetermined based on a reference image of an inspected-object referencearea, wherein the method further comprises generating the template,wherein the generating comprises downsampling a part of the referenceimage.

In accordance with an embodiment of the presently disclosed subjectmatter, there is yet further provided a method, wherein the referenceimage is generated from computer-aided design (CAD) data.

In accordance with an aspect of the presently disclosed subject matter,there is yet further provided a program storage device readable bymachine, tangibly embodying a program of instructions executable by themachine to perform a method for classifying a potential defectidentified within an inspection image of an inspected object, the methodcomprising the steps of determining an anchor location with respect tothe inspection image, based on a matching of a template and a portion ofthe inspection image, wherein an accuracy of the determining of theanchor location exceeds a resolution of the inspection image; based on amask which defines different segments within an area and on the anchorlocation, determining a distribution of the potential defect withrespect to one or more of the segments; and classifying the potentialdefect based on the distribution.

In accordance with an embodiment of the presently disclosed subjectmatter, there is yet further provided a program storage device, whereinthe inspected object is selected from a group consisting of anelectronic circuit, a wafer, and a photomask.

In accordance with an embodiment of the presently disclosed subjectmatter, there is yet further provided a program storage device, whereinthe determining of the distribution comprises determining thedistribution at an accuracy which exceeds the resolution of theinspection image.

In accordance with an embodiment of the presently disclosed subjectmatter, there is yet further provided a program storage device, whereinthe different segments correspond to parts of the inspected objecthaving different physical characteristics.

In accordance with an embodiment of the presently disclosed subjectmatter, there is yet further provided a program storage device, whereinthe classifying comprises classifying the potential defect according toa classification in which classes correspond to defect types whoseimplications on an operability of the inspected object differ.

In accordance with an embodiment of the presently disclosed subjectmatter, there is yet further provided a program storage device,comprising determining multiple anchor locations with respect to theinspection image, based on matching of the template and multipleportions of the inspection image; wherein accuracies of the determiningof the multiple anchor locations exceed the resolution of the inspectionimage; based on the mask and on the multiple anchor locations,determining distributions of the potential defect with respect to one ormore of the segments; and classifying the potential defect based onmultiple distributions determined for it.

In accordance with an embodiment of the presently disclosed subjectmatter, there is yet further provided a program storage device, furthercomprising selecting for further scanning potential defects which areclassified into certain classes, wherein the selecting includesrefraining from selecting potential defects classified into at least oneclass other than the certain classes; and selectively scanning, in aresolution which is higher than the resolution of the inspection image,at least one area of the inspected object which is selected based onlocations of the potential defects which are selected.

In accordance with an embodiment of the presently disclosed subjectmatter, there is yet further provided a program storage device, whereinthe mask is determined based on a reference image of an inspected-objectreference area, wherein the method further comprises generating thetemplate, wherein the generating comprises downsampling a part of thereference image.

In accordance with an embodiment of the presently disclosed subjectmatter, there is yet further provided a program storage device, whereinthe reference image is generated from computer-aided design (CAD) data.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to understand the invention and to see how it may be carriedout in practice, embodiments will now be described, by way ofnon-limiting example only, with reference to the accompanying drawings,in which:

FIGS. 1A and 1B illustrate two types of defects in an electroniccircuit;

FIG. 2 is a block diagram of a potential-defect analysis system that maybe used for classifying potential defects identified within aninspection image of an inspected object, according to an embodiment ofthe invention;

FIG. 3 is a flow chart of a computerized method for classifying an itemidentified within an inspection image of an inspected object, accordingto an embodiment of the invention;

FIG. 4 illustrates a template and a representation of a mask, accordingto an embodiment of the invention;

FIG. 5 illustrates relationships between entities used in theclassification, according to an embodiment of the invention;

FIG. 6 illustrates a repeating pattern in an inspection image, accordingto an embodiment of the invention;

FIG. 7 illustrates distribution of a potential defect identified in aninspection image of a wafer between multiple segments defined by a mask,according to an embodiment of the invention;

FIG. 8 illustrates a method for generating reference data that may beused for classification, according to an embodiment of the invention;and

FIG. 9 illustrates a process for generating reference data that may beused for classification, according to an embodiment of the invention.

It will be appreciated that for simplicity and clarity of illustration,elements shown in the figures have not necessarily been drawn to scale.For example, the dimensions of some of the elements may be exaggeratedrelative to other elements for clarity. Further, where consideredappropriate, reference numerals may be repeated among the figures toindicate corresponding or analogous elements.

DETAILED DESCRIPTION OF EMBODIMENTS

In the following detailed description, numerous specific details are setforth in order to provide a thorough understanding of the invention.However, it will be understood by those skilled in the art that thepresent invention may be practiced without these specific details. Inother instances, well-known methods, procedures, and components have notbeen described in detail so as not to obscure the present invention.

In the drawings and descriptions set forth, identical reference numeralsindicate those components that are common to different embodiments orconfigurations.

Unless specifically stated otherwise, as apparent from the followingdiscussions, it is appreciated that throughout the specificationdiscussions utilizing terms such as processing, calculating,determining, generating, setting, selecting, or the like, include actionand/or processes of a computer that manipulate and/or transform datainto other data, said data represented as physical quantities, e.g. suchas electronic quantities, and/or said data representing the physicalobjects. The term “computer” should be expansively construed to coverany kind of electronic device with data processing capabilities,including, by way of non-limiting example, a personal computer, aserver, a computing system, a communication device, a processor (e.g.digital signal processor (DSP), a microcontroller, a field programmablegate array (FPGA), an application specific integrated circuit (ASIC),etc.), any other electronic computing device, and or any combinationthereof.

The operations in accordance with the teachings herein may be performedby a computer specially constructed for the desired purposes or by ageneral purpose computer specially configured for the desired purpose bya computer program stored in a computer readable storage medium.

As used herein, the phrase “for example,” “such as”, “for instance” andvariants thereof describe non-limiting embodiments of the presentlydisclosed subject matter. Reference in the specification to “one case”,“some cases”, “other cases” or variants thereof means that a particularfeature, structure or characteristic described in connection with theembodiment(s) is included in at least one embodiment of the presentlydisclosed subject matter. Thus the appearance of the phrase “one case”,“some cases”, “other cases” or variants thereof does not necessarilyrefer to the same embodiment(s).

It is appreciated that certain features of the presently disclosedsubject matter, which are, for clarity, described in the context ofseparate embodiments, may also be provided in combination in a singleembodiment. Conversely, various features of the presently disclosedsubject matter, which are, for brevity, described in the context of asingle embodiment, may also be provided separately or in any suitablesub-combination.

In embodiments of the presently disclosed subject matter one or morestages illustrated in the figures may be executed in a different orderand/or one or more groups of stages may be executed simultaneously andvice versa. The figures illustrate a general schematic of the systemarchitecture in accordance with an embodiment of the presently disclosedsubject matter. Each module in the figures can be made up of anycombination of software, hardware and/or firmware that performs thefunctions as defined and explained herein. The modules in the figuresmay be centralized in one location or dispersed over more than onelocation.

FIG. 2 is a block diagram of an analysis system 200 that may be used forclassifying potential defects (or other types of identified items) whichare identified within an inspection image of an inspected object 50,according to an embodiment of the invention. While not necessarily so,the inspected object may be selected from a group consisting of anelectronic circuit, a wafer, and a photomask.

System 200 may obtain the inspection image in many ways. For example,system 200 may be combined with an inspection machine 210 that is usedto inspect the wafer or other types of inspected object (e.g. duringdifferent stages of manufacturing thereof). In another implementationsystem 200 may be connected to such an inspection machine, or theinspection image may be transmitted by an off-line device connected toonly one of the machines at a time. Also, system 200 may be aninspection machine into which some or all of the modifications and/orfeatures discussed below have been integrated.

As will be discussed below in more detail, one or more of the componentsof system 200 may be used to classify potential defects that weredetected in a scanned image of the wafer. This determined classificationmay later be used in the manufacturing the wafer, and/or in later stagesof inspection of the wafer. Some of the ways in which system 200 mayoperate will become clearer when viewed in the light of method 500discussed below.

System 200 includes pattern matcher 220 which is configured to determinean anchor location with respect to the inspection image, based on amatching of a template and a portion of the inspection image. Forexample, pattern matcher 220 may be configured to compute a correlationbetween the template and different portions of the inspection image, andto define the anchor location based on the portion whose correlationwith the template is the highest. Optionally, pattern matcher 220 may beconfigured to select within the inspection image a cell-area of apredetermined cell-size (e.g. selecting the area with the highestcorrelation, based on results of the correlation). It should be notedthat while not necessarily so, pattern matcher 220 may be configured todetermine the anchor location at an accuracy which exceeds a resolutionof the inspection image. Examples of ways in which pattern matcher 220may operate are discussed in further detail in relation to stages 530and 540 of method 500.

The template may be received from reference data input interface 202, ormay be generated by a component of system 200, such as image processingmodule 230. The inspection image may be received via inspection resultsinterface 204, or may be acquired by imaging system 210 (also denoted“inspection machine”).

Optionally, system 200 may include an image processing module 230 thatis configured to upsample the area 110 to a resolution higher than theresolution of the inspection image. Examples of ways in which imageprocessing module 230 may operate are discussed in further detail inrelation to stage 550 of method 500.

Distribution analysis module 240 is configured to determine, based onthe anchor location and on a mask which defines different segmentswithin an area, a distribution of the identified item (e.g. thepotential defect) with respect to one or more of the segments of a mask.Examples of ways in which distribution analysis module 240 may operateare discussed in further detail in relation to stage 560 of method 500.

As will be discussed below in more detail, according to an embodiment ofthe invention, distribution analysis module 240 may be configured todetermine the distribution in a sub-pixel level with respect to theresolution of the mask.

Classifier 250 of system 200 is configured to classify the identifieditem (e.g. the potential defect) based on the distribution determinedfor it by distribution analysis module 240 (and possibly also based onclassification rules, e.g. as discussed below). It should be noted thatclassifier 250 may use additional factors pertaining to the potentialdefect during its classification, such as grey level, size, shape, etc.

System 200 may include a tangible storage 260 (e.g. a hard-drive disk, aflash drive, etc.) for storing the classification (or part thereof—e.g.only the defects which were classified as noteworthy) to a tangiblestorage. System 200 may also include an output interface 270 fortransmitting the classification (or part thereof) to an external system(e.g. over cable connection or over wireless connection), wherein thatexternal system may in turn act based on the classification.

System 200 may also include an inspection module, which may be theaforementioned inspection machine 210 which provides the aforementionedinspection image by scanning of the inspected objects such as thewafers, and may alternatively be posterior inspection module 280 that isconfigured to inspect the wafer (or other inspected object) in higherresolution than that of the inspection image. This inspection module isconfigured to selectively scan, in a resolution higher than theresolution of the inspection image, areas of the inspected object whichare selected based on the locations of identified items (e.g. potentialdefects) which are classified into certain classes but not into at leastone of the other classes (i.e. refraining from selecting potentialdefects classified into at least one class other than the certainclasses). The field of view of posterior inspection module 280 may benarrower than that of inspection machine 210, but this is notnecessarily so.

In such a case, the areas selected for further scanning may be selectedbased on the locations of potential defects which are classified intocertain classes but not into at least one of the other classes. Forexample, the scanning in the higher resolution may be carried out aroundthe locations of the possible defects classified as “short gate”, butnot around the locations of the possible defects classified as “edgeroughness”.

It should be noted that inspection machine 210 and/or posteriorinspection module 280, if implemented, may be implemented as inspectionmachines of various types, such as optical imaging machines, electronbeam inspection machines, radars, LIDARs and so on.

Generally, identifying defects in a wafer (or in another inspectedobject) may be implemented using different techniques, among which areoptical inspection and electron beam inspection. Utilization of system200 may facilitate the use of more than a single inspection technique.For example, an initial inspection of the wafer is firstly carried outrelatively quickly and in a coarse manner by inspection system 200 (e.g.using an optical inspection or an electron beam inspection set forcoarse and fast inspection). Later, some of the potential defects foundin the initial inspection (selected based on the classification resultsof classifier 250) are then studied again using a relatively slower butmore exact inspection. Such posterior scanning may be executed either inanother mode of inspection machine 210, or in a different posteriorinspection module 280 (in a process also referred to as “reviewing”,e.g. by DRSEM—Defect Review Scanning Electron Microscope).

Referring to the mask mentioned above, optionally the mask may definesegments of multiple types, wherein the number of types is smaller thanthe number of segments. In such implementations, distribution analysismodule 240 may be configured to determine a type-based distribution ofthe potential defect between one or more of the types; and classifier250 may be configured to classify the potential defect based on thetype-based distribution. However, for simplicity of explanation, it willbe assumed that each segment is handled independently of other segments.

While not necessarily so, the different segments may correspond to partsof the inspected object having different physical characteristics, suchas the material they are made from, their reflective value, theirelectrical conductance, and so on. As discussed in greater detail below,classifier 250 may be configured to classify the potential defect (orother identified item) according to a classification, in which classesthat correspond to defect types, whose implications on operability ofthe inspected object, differ.

In some implementations, pattern matcher 220 may be configured todetermine multiple anchor locations with respect to the inspectionimage, based on matching of the template and multiple portions of theinspection image. Likewise, pattern matcher 220 may optionally beconfigured to select within the inspection image multiple cell-areas ofthe predetermined cell-size, based on correlation of the template tomultiple different portions of the inspection image. As in the case ofselecting a single anchor location, the accuracies of the determining ofthe multiple anchor locations may exceed the resolution of theinspection image.

Distribution analysis module 240 may be configured in suchimplementation to determine, based on the mask and on the multipleanchor locations, distributions of the potential defect with respect toat least one segment of the mask. Also, classifier 250 may be configuredto classify a single potential defect based on multiple distributionsdetermined for that single potential defect by distribution analysismodule 240.

Referring to the example and terminology of FIG. 5 which is discussedbelow, since a single identified item may be included in two or morecell-areas processed, according to an embodiment of the inventionclassifier 250 may be configured to classify the potential defect basedon distributions determined for it in two or more different cell-areas.

Instead of receiving the mask and the template from an external systemvia reference data input interface 202, system 200 may include areference data generator (not illustrated) which is configured to definethe mask based on a reference image of an inspected-object referencearea. A reference data generator of system 200 may be configured todownsample a part of the reference image, and to generate the templatebased on a result of the downsampling. While the template may have alower resolution than the reference image and/or the inspection image,it is noted that image processing techniques other than a simpledownsampling may also be implemented in the generating of the template,in addition to or instead of the downsampling. The reference datagenerator may be configured to generate the reference-image fromcomputer-aided design (CAD) data, or to use a scanning image as areference.

As aforementioned, some of the ways in which system 200 and itscomponent may operate are discussed in greater detail with respect tomethod 500.

System 200 may be implemented on a computer (such as a PC), e.g. thecomputer which implements the overall classification (Image BasedAttributing, IBA) of the runtime inspection results, but this is notnecessarily so. Each of the modules or components of system 200 may beimplemented in software, hardware, firmware, or any combination thereof.Additionally, system 200 may also include other components that are notillustrated, and whose inclusion will be apparent to a person who is ofskill in the art—e.g. a power source, a display, etc.

FIG. 3 is a flow chart of computerized method 500 for classifying anitem identified within an inspection image of an inspected object,according to an embodiment of the invention. Referring to the examplesset forth in the previous drawings, method 500 may be carried out bysystem 200. Different embodiments of system 200 may implement thevarious disclosed variations of method 500 even if not explicitlyelaborated. Likewise, different embodiments of method 500 may includestages whose execution fulfills the various disclosed variations ofsystem 200, even if succinctness and clarity of description did notnecessitate such repetition.

Method 500 may be implemented for various types of inspected objects,from a very minute scale (e.g. millimetric or nanoscale objects) tolarger objects such as geographical area imaged from an airplane or froma satellite. The item identified may be a specific item or a groupthereof (e.g. looking for tanks in an areal image), but may also be, forexample, a deviation from an expected pattern (such as a hole in atextile fabric, or a potential manufacturing defect in a wafer).

In order to clarify the disclosure, different stages of method 500 wouldbe exemplified using a revised example of an inspected object which isselected from a group consisting of an electronic circuit, a wafer, anda photomask (a partially transparent plate which may be used for themanufacturing of electronic circuits or other objects in a processimplementing transmitting light through such a photomask, such asphotolithography). The one or more items identified within theinspection image would be exemplified in such cases using the example ofpotential defects. A person who is of ordinary skill in the art wouldnevertheless understand that this is merely but one example, and thatmany other types of inspected objects and items identified withininspection images thereof (such as the examples provided above) may beimplemented.

Method 500 may include stage 510 of receiving inspection results whichincludes the inspection image in which at least part of the inspectedobject is imaged. Referring to the examples set forth in the previousdrawings, stage 510 may be carried out by an inspection result interfacesuch as inspection results interface 204 of system 200.

The inspection results may further include item identificationinformation identifying one or more items which were identified withinthe inspection image. The item identification image may include one ormore of the following:

-   -   a. Location information of each of the one or more items (e.g.        indication of one or more pixels in the inspection image which        correspond to the item, or even indication in a sub-pixel        accuracy);    -   b. Size information, indicating size of the item (e.g. indicated        in pixels);    -   c. Type information, identifying initial classification of the        item;    -   d. Small image excerpts of the inspection image, each of which        includes one or more of the items;    -   e. Grade of the item in one or more grading systems (e.g.        indication of the likelihood of defectiveness of the indicated        potential defect).

Clearly, the item identification information may include additionalinformation. In an example in which the items are potential defectsidentified in an inspection image of a wafer, the item identificationinformation may also be referred to as a defect list.

Clearly, the receiving of the inspection image may be replaced by astage of capturing (or otherwise generating) the inspection image. Forexample, this may be implemented by optical photography, by electronbeam inspection, by laser beam inspection, and so on. Likewise, itemidentification information may not only be obtained by receiving samefrom an external entity, but may also be obtained by image-processingthe inspection image, and generating item identification informationbased on the results of the image processing.

Method 500 may also include stage 520 which includes receiving referencedata which includes at least one of the following data entities, whosecontent and use will be described below: template, mask, andclassification rules. In another implementation, one or more of thosedata entities may be created as part of the method. Creation of suchdata entities is discussed in relation to method 600, which may beimplemented as part of method 500, or independently thereof. Referringto the examples set forth in the previous drawings, stage 520 may becarried out by a reference data input interface such as reference datainput interface 202 of system 200. The template, mask and theclassification rules may be user defined, machine defined, and so on.

The classification rules will be discussed in due course, as part of thediscussion of their utilization in method 500. The template and maskdata entities will be introduced with reference to FIG. 4.

FIG. 4 illustrates a template 300 and a representation of a mask 400,according to an embodiment of the invention. According to such anembodiment of the invention, template 300 is an image. The templateimage may be an actual image of a part of the inspected object, or asimilar image. For example, the image data of template 300 may beobtained by actually imaging a part of the inspected object (or asimilar reference object, e.g. as discussed below). In other embodimentsthe image data of template 300 may be obtained by processing design data(e.g. CAD data). As will be discussed below, the image data of thetemplate may be obtained by downsampling (i.e. reducing the spatialresolution of) at least a part of an original image of higherresolution. The resolution of the received template may be the same asthe resolution of the runtime inspection image, but this is notnecessarily so.

The mask (represented by the region denoted 400) defines differentsegments 410 within a predefined area (also referred to as “cell-sizedarea”. While conversion of units may be applied, the term “size” in thecontext of the cell-sized area pertains to coordinates of the inspectionplane, such as the plane of the inspected layer of the wafer when thelater is inspected).

The different segments 410 may be of the same or different sizes, and ofthe same or different shapes. While the segments 410 are illustrated asrectangular, this is not necessarily so, and segments 410 of othershapes may also be implemented. The various segments 410 may cover theentire area of the mask, but this is not necessarily so.

The size of the mask may be defined in response to external data, orotherwise. For example, if the inspected object includes a repeatingpattern (e.g. as exemplified in FIG. 6), the size of the mask maycorrespond to the size of the repeating area (denoted 180 in FIG. 6), orto a part thereof.

Optionally, the segments 410 defined by the mask 400 may be of multipletypes (e.g. wherein the number of types is smaller than the number ofsegments). This is exemplified in that the three segments denoted 410(1)are of the same type. Each of the segments 410 (or of the differenttypes of segments) may correspond to parts of the inspected object thathave different physical characteristics. For example, different levelsof electrical conductivity in the inspected object may correspond todifferent types of segments.

The various segments 410 (or the different types of segments) may bedefined according to the type (or types) of items whose classificationis achieved by method 500. For example, if method 500 is used forclassification of potential defects, different segments 410 (or typesthereof) may correspond to different parts of the inspected object (e.g.wafer, electronic circuit or photomask) which have differentsusceptibility to defects, and/or different likelihood to have differenttypes of defects. It should be noted that in some implementations, asingle segment may cover areas which are not connected to each other.For example, in such an implementation, all of the segments 410(1) maybe considered as a single segment, and not only as several segments ofthe same type.

The mask may be stored in different formats. For example, it may bestored as an image (in which different colors correspond to differenttypes of segments), as a table (e.g. denoted starting point, dimensions,and possibly type for each of the segments), in vectorial format, and soon.

In FIG. 4, the template 300 and the mask represented (denoted 400) bothcorrespond to a similarly sized area of the inspected object (albeitpossibly having different resolutions). However, in otherimplementations—e.g. as exemplified in FIG. 5—the physical areas towhich the template and the mask pertain may be different from eachother. In such a case, one of those corresponding areas may be includedwithin the other (as exemplified in FIG. 5), partly overlapping, andeven non-overlapping.

Reverting to FIG. 3, method 500 may include stage 530 of correlating thetemplate and at least a portion of the inspection image. While thetemplate is not necessarily identical to any of the correlated portionsof the inspection image, its correlation to some of the portions wouldbe higher than its correlation to others. For example, the correlationof the template to one of the portions may be relatively high, and atleast higher than portions of the inspection image which are slightlyshifted with respect to said portion. Referring to the examples setforth in the previous drawings, stage 530 may be carried out by apattern matcher such as pattern matcher 220 of system 200.

It should be noted that the correlation does not necessarily conform tothe pixels of the inspection image. It is noted that the correlation maybe implemented in sub-pixel accuracy (e.g. in accuracies of thousandthof a mask pixel). As can be seen in the example of FIG. 5, area 130,which is assumed in the example to be the one best correlating thetemplate, is not located on the grid representing the pixels of theinspection image 100, but is rather located (hence determined) in asub-pixel resolution. The correlating of stage 530 may be carried outbased on the defect list (e.g. only in areas of the wafer in whichpotential defects were detected) or regardless thereof.

Stage 540 of method 500 includes determining an anchor location withrespect to the inspection image, based on a matching of a template and aportion of the inspection image. Referring to the examples set forth inthe previous drawings, stage 540 may be carried out by a pattern matchersuch as pattern matcher 220. The anchor location may be, for example,the location of anchor 120 (which may be a dimensionless point oranother kind of an anchor). It should be noted that while the matchingof the template and the portion of the image (e.g. area 130 illustratedin FIG. 5) may be accomplished by a correlation (such as in stage 530),in other implementations other matching techniques may be implemented(such as pattern detection, and so on). While not necessarily so, insome implementations an accuracy of the determining of the anchorlocation exceeds a resolution of the inspection image.

Optionally, method 500 may include stage 542, which includes selectingwithin the inspection image a cell-area of a predetermined cell-size,wherein the selecting is based on the correlation of the template and atleast a portion of the inspection image (or other type of matchingbetween the two). In a way, if a geometrical relationship between theanchor location and the cell-sized area is known in advance (e.g. arrow122 of FIG. 5, as well as the size of area 110), the selecting of thecell-area in stage 542 may be a direct by-product of the selecting ofthe anchor location in stage 540.

While conversion of units may be applied, the term “size” in the contextof the cell-area pertains to coordinates of the inspection plane (e.g.the plane of the inspected layer of the wafer when the latter isinspected). While the cell-size may be identical to the area-sizedefined by the mask, this is not necessarily so, and the mask may definethe segments within a larger area, that includes the aforementionedcell-sized area. Referring to the examples set forth in the previousdrawings, stage 540 may be carried out by a correlator such as patternmatcher 220.

It should be noted that if the resolution of the mask is higher thanthat of the cell-area, then the two areas would have different pixelsize, even if pertaining to similar sized areas in the inspection plane.Referring to the size of the template in the coordinates of theinspection plane, the size of the cell-area may be smaller, similar, orlarger than the area of the template.

In some implementations of the invention, a decision rule may beimplemented prior to execution of stage 540, according to which if noarea of the inspection image may be matched to the template in asufficiently successful manner, the method is terminated. For example,if no portion of the inspection image is found to have a correlationscore which exceeds a predetermined threshold, an anchor location is notnecessarily determined. While one possible result of stage 530 (oranother stage of matching the template to an area of the inspectionimage) is singling out of at least one portion of the inspection imagebased on its correlation to the template, another possible result isthat no area is found to be matching.

Reverting to stage 540, optionally, an anchor may be defined for any oneof those singled out portions of the inspection image (referring to theexample of FIG. 5, an anchor 120 may be defined within or otherwise withrespect to area 130 selected within inspection image 100). If stage 542is implemented, the selecting of that stage may include selecting theportion (or portions) of the inspection image singled out in stage 530,but may also include selecting another area of the inspection image.Referring again to the example of FIG. 5, the area 110 selected in stage542 is larger than the area 130 selected in stage 530. Area 110 (alsoreferred to as “cell-area” 110) may be defined with respect to anchor120 which is determined in stage 540 (for example it may be defined withrespect to area 130, which was singled out of the inspection image,based on the matching).

As mentioned above, the inspected object may include a repeating pattern(e.g. as exemplified in FIG. 6). If the template corresponds to a part(or all) of the area that is repeated in the pattern, many similarportions of the inspection image may be matched to the template (e.g.correlated, as in stage 530), and many anchor locations may bedetermined correspondingly in stage 540.

For example, the inspected object imaged in the inspection image 100 ofFIG. 6 includes a repeating pattern that includes multiple occurrencesof a pair of vertical lines. While those multiple areas are notidentical, each matching of the template to one of those areas maynevertheless result in a defining of a separate anchor location in stage540 (e.g. based on a correlation of different portions of the inspectionimage to a template, which is not shown).

Multiple portions of the inspection image that are similar to thetemplate may yield determination of multiple anchor location (andpossibly also to a selection of multiple cell-sized areas) even if thesimilar portions do not form a recurring pattern. All the more so, insome implementations multiple templates may be implemented (e.g. whichcorrespond to different recurring patterns in the wafer), whereinmultiple anchor location may be determined in stage 540 based onmatching of various areas of the inspection image to multiple templates.In such a case, masks of different sizes may be used for such areas. Ifmultiple anchor locations areas are determined in stage 540, some or allof the following stages of method 500 may be repeated for some or all ofthose multiple anchor locations.

As aforementioned, the resolution of the mask may be different, andparticularly may be higher, than that of the inspection image. That is,the segments defined in the area of the mask may be defined in aresolution higher than that of the inspection image, and particularly—aresolution higher than that of the one or more selected cell-areas, ifselected at optional stage 542.

In such a case, method 500 may optionally include stage 550 ofupsampling the cell-area of stage 542 to a resolution higher than theresolution of the inspection image. Especially, stage 550 may includeupsampling the cell-area to the resolution of the mask. Referring to theexamples set forth in the previous drawings, stage 550 may be carriedout by an image processing module such as image processing module 230 ofsystem 200.

Referring to the example of FIG. 5, area 110 (hereinafter also simplyreferred to as “cell” or as “area 110”) is an X_(cell) by Y_(cell)pixels sized area. It can be seen that while X_(cell) and Y_(cell) maybe an integer, the location of the area 110 does not have to readexactly on the grid of pixels (denoted 102) of the inspection image 100.For example, the best matching between the template and the inspectionimage may be achieved for an area that is defined in non-integer pixelcoordinates. In an additional example, the distance between the locationof anchor 120 and the corresponding area 110 may be defined innon-integer pixel coordinates.

On the right side of FIG. 5, an upsampled version of area 110 isillustrated (denoted 110′), overlapped with a representation of the mask400. As can be seen, in the resolution of the inspection image the sizeof the area 110 (also referred to as “cell”) is 6 by 6 pixels. The sizeof the upsampled version thereof is 15 by 15 pixels. In the illustratedexample, the upsampling of the area 110 into area 110′ includingincreasing the resolution by a factor of N=2.5. It is again noted thatwhile upsampling of a selected cell area may be implemented in someimplementations of the invention, it is not necessarily so. As notedabove, in some implementations no such cell-area is selected or defined.

If implemented, the upsampling may include a simple linearinterpolation. In other implementations, other types of interpolationtechniques may be implemented—such as bicubic interpolation, bilinearinterpolation, nearest-neighbor interpolation, and so on.

By way of illustration, highlighted pixel 140 of the inspection imagecorresponds to an N by N pixels area, denoted 140′, in mask-resolution(if indeed implemented as a raster image), in an example in which alinear interpolation is implemented.

Highlighted pixel 140 may represent, for example, a location of apotential defect detected during an analysis of the inspection image. Itshould be noted, however, that potential defects may also possibly bedefined as larger or as smaller than a single pixel.

The different segments 410 of the mask are illustrated as defined in asub-pixel resolution. This may be implemented for example in a vectorialrepresentation of the mask. It should be noted that in someimplementations, segments 410 of the mask may be defined only inresolution of whole pixels—e.g. if the mask is defined as a rasterimage. Different segments of the mask are enumerated in FIG. 5 as410(3), 410(4), 410(5), and 410(6). The areas denoted 410(0) may bedefined in the mask as segments (thereby having the segments coveringthe entire area), or not be defined at all (thereby having the segmentscovering the area only partially).

Reverting to FIG. 3, method 500 continues with stage 560 of determining,based on the mask (which, as aforementioned, defines different segmentswithin an area) and on the anchor location, as well as distribution ofthe identified item (e.g. the potential defect) with respect to one ormore of the segments. Referring to the examples set forth in theprevious drawings, stage 560 may be carried out by a distributionanalysis module such as distribution analysis module 240 of system 200.

Referring to the example of FIG. 5, it is noted that a relationshipbetween the segments of the mask and the identified item may befacilitated by knowledge of a relationship between the location ofanchor 120 and at least one reference point of the mask (this isillustrated by arrow 122, and may be part of the reference data) andknowledge of a relationship between the location of anchor 120 and atleast one reference point on the identified item (this is illustrated byarrow 124, and may be determined since both anchor location and thelocation of the identified item may be defined in coordinates of theinspection image 100).

It can be seen that the area 140′ (which is an enlarged analogue ofpixel 140 of the inspection image) is distributed between segments410(0), 410(3) and 410(6) of the mask. As aforementioned, the size ofthe potential defect (or other item) may also be more or less than onepixel. As aforementioned, the identified item (e.g. the potential defectin the wafer) may be associated with location information and possiblyalso size information and/or grade, as obtained with the itemidentification information.

It should be noted that the determining of the distribution may includedetermining the distribution at an accuracy which exceeds the resolutionof the template, of the inspection image, and/or the accuracy in whichthe mask is defined. This enables, inter alia, to provide classificationbased on high resolution rules, while not requiring an increase of theresolution of the scanning.

In the example of FIG. 7, the size of the item (e.g. the potentialdefect) as identified in the inspection image is not a single pixel (asin the example of FIG. 5), but rather four pixels, illustrated by area150 in FIG. 7. The upsampled analogue of area 150 is area 152. Area 152is illustrated as divided into four quarters, but this is done forillustratory reasons only, and, as aforementioned, the determining ofthe distribution is done in a higher resolution.

As can be seen, slightly more than half of area 152 overlaps withsegment 410(7), and slightly less than half overlaps segment 410(8).This is reflected by the determined distribution (denoted 900) in whichsegment type 1 (which corresponds to segments 410(8)) receives a scoreof just under 2, and segment type 2 (which corresponds to segments410(7)) receives a score of just over 2. In the given example, thescores in the distribution are given in units equal to the originalpixels of the inspection image, but a person who is of skill in the artwould understand that any other method of determining the distributionmay be implemented. For example, the distribution may be determined inpercents, in pixels (usually a fractional quantity), in nanometers, andso on, wherein such definition would usually be similar to that in whichthe classification logic was defined.

It is noted that while the area that corresponds to the identified itemmay be distributed between multiple segments of the mask, this is notnecessarily the case, and the entire area may correspond to a singlesegment of the mask.

As aforementioned, the mask may define segments of multiple types (e.g.segments 410(7) and segments 410(8)), wherein the number of types issmaller than the number of segments. In such a case the determining ofthe distribution may include determining a type-based distribution ofthe potential defect between one or more of the types—e.g. asexemplified in FIG. 7. The classifying of stage 570 in such a case wouldbe based on the type-based distribution.

Reverting to FIG. 3, method 500 further includes stage 570 ofclassifying the identified item (e.g. the potential defect) based on thedistribution determined for it. The classifying of stage 570 may befurther based on the classification rules (which may also be referred toas “binning rules”, “binning logic” and “classification logic”).Referring to the examples set forth in the previous drawings, stage 570may be carried out by a classifier such as classifier 250 of system 200.

The classification rules may indicate to which class an identified item(e.g. a potential defect) should be classified, based on thedistribution for it. By way of example, referring to the illustration ofFIG. 7, the classification rules may indicate that a potential defectidentified in the inspection image of the wafer should be classified asinsignificant if less than 50% of it corresponds to segment 410(7), butclassified as significant if more than 50% of it corresponds to thissegment.

Table 1 is an example of binning logic, according to an embodiment ofthe invention. The final classification is shown in the Class Namecolumn, and the rules by which this classification is selected arerepresented by the symbols (1, 0, X) in the middle five columns “1”represents that the segment must be found, “X” represents that thesegment may or may not be found, and “0” represents that the segmentmust not be found. The priority indicates that, in implementation, ifthe conditions for two or more rules are fulfilled, the rule with thelower priority index would be selected. For example, fulfillment of theconditions for selection of rule 2 would also qualify for rule 3.However, since rule 2 has a lower priority index, it will be selectedover rule 3.

TABLE 1 Seg- Seg- Seg- Seg- Priority ment 1 ment 2 ment 3 ment 4 OtherClass name 1 0 0 0 1 0 4 only 2 0 0 0 1 1 4 and other 3 1 1 0 X X DOI 14 1 1 1 X X DOI 2

The classification of stage 570 may be just a part of a largerclassification process, which may depend on other parameters as well.For example, the classification of the identified item (e.g. thepotential defect of the wafer) may also depend on its size, color,bright field/dark field, etc. This classification may be implemented bycombining method 500 for classification in a more general binningapplication, such as those used for binning of potential defects inwafer inspection in prior art machines.

As aforementioned, the different types of the segments may correspond toparts of the inspected object which have different physicalcharacteristics (e.g. electrical characteristics, different internalconstruction, made of different material, and so on). In such a case,the classifying may include classifying the potential defect intoclasses that correspond to defect types which have electricallydifferent implications on electrical operability of the inspectedobject. This may include, for example, classifying potential defects ina wafer or a photomask into “edge roughness” defects versus “short gate”defects.

As indicated above, method 500 may include determining within theinspection image multiple anchor locations (and possibly also multiplecell-areas of the predetermined cell-size), based on matching of thetemplate to multiple different portions of the inspection image(possibly only in areas of the inspection image in which defects weredetected, but not necessarily so). In such a case, the stages ofdetermining of a distribution and classifying (as well as potentiallyother stages of method 500) may be repeated for the multiple cell-areas.

Reverting to the determined distribution, it is noted that since thecell-areas may partly overlap each other (e.g. in a repeating pattern,such as exemplified in FIG. 6), a single item (e.g. potential defect)may be processed for two instances of the mask (in stage 560), and itsdefect distribution will be different in each instance. Theclassification rules in such a case may include instructions for how toproceed in such situations (e.g. prefer the worst-case scenario, averagethe results, have the potential defect appear twice in the results,etc.).

Generally, according to an embodiment of the invention, the classifyingmay include classifying the potential defect based on two or moredistributions determined, based on two or more anchor locations.

Once the item (or items) has been classified, actions may be continuedbased on the classification. Method 500 may conclude with storing theclassification (or part thereof—e.g. only the defects which wereclassified as noteworthy) to a tangible storage and/or with transmittingthe classification (or part thereof) to an external system which, inturn, may act based on the classification.

However, method 500 may also continue with other actions that are basedon the classification. For example, method 500 may continue with stage580 of selectively scanning areas of the inspected object in aresolution higher than the resolution of the inspection image. In such acase, the areas selected for further scanning may be selected based onthe locations of potential defects which are classified into certainclasses but not into at least one of the other classes. For example, thescanning in the higher resolution may be carried out around thelocations of the possible defects classified as “short gate”, but notaround the locations of the possible defects classified as “edgeroughness”.

Referring to the examples set forth in the previous drawings, stage 580may be carried out by an inspection machine such as inspection machine210, or by a posterior inspection module (which may be anotherinspection machine), such as posterior inspection module 280. Forexample, if the inspected object is indeed a wafer, the inspection imagemay be obtained using Electron Beam Inspection (EBI) in a firstresolution, while the potential defects selected, based on the way inwhich they were classified, may be further inspected in much higherresolution by a Defect Review Scanning Electron Microscope (DRSEM).

The wafer (or specific dies thereof) may be declared as operational ornonoperational based on the high resolution inspection of the selectedpotential defects. Inspecting only potential defects classified based onthe mask, while not inspecting other potential defects (which may belocated in “uninteresting” areas of the wafers) saves time andresources, and may also improve the results of the inspection. Forexample, scanning less areas of the wafer would lead to lessaccumulation of electrical charge resulting from the electrons beamed bythe electron beam scanning apparatus.

Referring to method 500 as a whole, it may include correlating areas ofa runtime inspection image of a wafer to a template (this stage is alsoreferred to as “template anchoring”). Based on this correlation, areasof the inspection image are selected and later upsampled to provideareas which correspond to the mask. While the determining of the maskmay be a relatively long process, as it may serve for runtime inspectionof many inspected wafers (or any other inspected objects)—the runtimeinspection in such a scenario should be relatively quick (because it isrepeated many times). In order to achieve this rapidity, the inspectionmay be carried out in a relatively low resolution.

It should be noted that while providing additional information usefulfor the classification process, method 500 does not necessitate anyincrease in the runtime inspection time, and that while it may provideinformation in sub-pixel accuracy, it does not require any reduction ofthe runtime inspection pixel size.

It is noted that while the examples above pertain to electron beamscanning, the disclosed techniques may also be implemented for othertypes of inspection or imaging (e.g. optical, Radar, sonar, etc.).Likewise, while some of the examples above pertain to inspection of anelectronic circuit such as wafers, the disclosed techniques may also beimplemented for other types of inspection objects, whether in thenanometric scale or in other scales.

As mentioned above, the template and the mask utilized in method 500 maybe created in different ways. For example, the mask may be determinedbased on a reference image of an inspected-object reference area. Themethod in such a case, as will be discussed below, may includedownsampling at least a portion of the reference image to provide thetemplate which has a lower resolution. It is noted that apart fromdownsampling, the generating of the template may also include additionaltypes of image processing.

The inspected-object reference area may be a part of the same inspectedobject (e.g. another die in the same wafer), or may belong to anotherinspected object (e.g. another wafer of the same batch, or of anotherbatch). The reference image in such a case may be generated by the sameinspection machine used for the obtaining of the inspection image ofmethod 500. In another implementation, the reference-image may begenerated from computer-aided design (CAD) data.

The creating of the mask and/or the template used in method 500 may becarried out according to the process of method 600.

FIG. 8 illustrates method 600 for generating reference data that may beused for classification (e.g. as in method 500), according to anembodiment of the invention.

Method 600 starts with stage 610 of obtaining a reference image of aninspected-object reference area. If the reference image is an image ofan actual object (and is not based on CAD data, for example), a highresolution image of the reference object (which may be selected from agroup consisting of an electronic circuit, a wafer, and a photomask, butnot necessarily so) may be obtained—either by direct scanning,inspecting, etc., or by receiving the same from another system. The highresolution image may include imaging data of the entire referenceobject, or just of the reference area. Referring to the examples setforth in the previous drawings, stage 610 may be carried out by aninspection results interface such as inspection results interface 204,or by an inspection machine such as inspection machine 210.

For example, the high resolution reference image may be an image of adie, or of a part of a die. The high resolution reference image may begathered from a high-resolution inspection process and/or from acomputer-aided design (CAD) file. By way of example, if thehigh-resolution image is gathered by increasing resolution on the e-beaminspection tool or by using a SEM imaging system.

Once the high-resolution image is obtained, it may be used for thedefinition of information which may later be used in the actualclassification of detected possible defects. Method 600 may continuewith stage 620 of identifying a pattern of interest in the referenceimage. Such a pattern of interest is also referred to as a “GoldenCell”. Such a pattern of interest may be identified by a person or maybe automatically generated. It is noted that such a pattern or “GoldenCell” may be repeated multiple times in the inspection image (possiblyin a periodical recurrence pattern), but this is not necessarily so.Referring to the examples set forth in the previous drawings, stage 620may be carried out by an image processing module such as imageprocessing module 230.

Stage 630 of method 600 includes generating a mask which definesdifferent segments in a cell-sized area. In the case of a referenceimage that includes a repeating pattern, the size of the cell may besubstantially similar to that of the repeating pattern (or to a sub-areathereof, e.g. one which is defined as the pattern of interest in stage620). It is noted that in some implementations, the size of the mask maybe smaller than that of the repeating pattern (possibly significantlyso) or larger than the size of the repeating pattern). Referring to theexamples set forth in the previous drawings, stage 630 may be carriedout by a mask generation module such as mask generation module 290 ofsystem 200.

Within the mask, different segments are defined. Such different segmentsmay be defined for different reasons. For example, segments may bedefined to correspond to different functionalities of the electroniccircuit (or other item) at the corresponding area or other areas ofinterest within the Golden Cell pattern. In another example, segmentsmay be defined to correspond to different susceptibility to defects. Thenumber of areas defined in the mask may be different for differentinspected objects (e.g. for different electronic circuits), and may bedifferent depending on the usefulness of classification based on suchsegments to the detection and/or analysis of defects at a later time.

For example, while in some implementations the number of distinctsegments in the mask may be three, five, or eight, in otherimplementations dozens and even hundreds of areas may be defined. Thedefining of the mask segments may be carried out by a person or by acomputer (e.g. based on CAD data).

It should be noted that if indeed the mask is generated in a resolutionhigher than that which is later used for the runtime inspection inmethod 500, the segments of the mask are defined in stage 630 at arelatively high resolution (e.g. at the resolution of thehigh-resolution reference image). While in some implementations thesegments defined in the mask are non-overlapping and cover between themthe entire area of the mask, this is not necessarily so and some areasof the mask may not belong to any segment.

It should be noted that while the runtime inspection in method 500, inwhich the identified items (e.g. the potential defects) will beclassified, may be carried out in a relatively lower resolution (e.g.having a pixel size corresponding to ×2, ×3 or more the high resolutionpixel size), the utilization of a high resolution mask (together withupsampling of part of the inspection image) enables classification ofsuch identified items based on their locations in relation to thehigher-resolution mask—which is far more exact.

If indeed the resolution of the reference image is higher than that ofthe inspection image used in method 500, method 600 may further includestage 640 of processing the high-resolution reference image to provide alower resolution template corresponding to at least an area ofhigh-resolution image. Apart from downsampling at least a part of thereference image, the generating of the template may includeimplementation of additional image processing techniques such assoftening, smoothing, edge enhancements etc. According to an embodimentof the invention, this step is mandatory if the mask is defined on aresolution that differs from the inspection resolution used for thegenerating of the inspection image of method 500. Referring to theexamples set forth in the previous drawings, stage 640 may be carriedout by an image processing module such as image processing module 230 ofsystem 200.

The processing of stage 640 may be implemented by a decimation imageprocessing algorithm or other down-sampling algorithm, but this is notnecessarily so. The template (also referred to as “lower resolutionreference image”) may be used for detection of the recurring pattern(e.g. by identifying a part of it, an anchor) in a run-time inspectionimage, and therefore for determining a spatial correlation between themask and the run-time inspection image.

Optionally, the resolution of the template may be lower than that of themask (and of the high-resolution image), and corresponds to the intendedrun-time inspection resolution. More than one template may be generated,e.g. in different lower resolutions—to be used in different runtimeinspection resolutions. By way of example, the resolution of thetemplate may have a pixel size (height or width) corresponding to 100 nmwhile the mask resolution may have a pixel size corresponding to 70 nm.An example of a processing of part of the high-resolution referenceimage to provide a lower resolution template is exemplified in FIG. 9.

The ratio between the 1-D pixel dimension between the template and themask may differ in various implementations of the invention (e.g. 1:2,1:4, 1:7, 1:15, etc.). It is noted that the ratio between thecorresponding pixel areas is a second power of that ratio (e.g. 1:4,1:16, 1:49, etc.).

Method 600 may further include stage 650 of defining of classificationlogic in which different classification rules apply to differentdistributions between mask segments of the mask. The defining of theclassification rules may depend on the specific mask defined, but notnecessarily so. For example, the classification rules may be definedbased on the relative portion covered by each of the segment types inthe mask. Alternatively, the classification rules may be irrespective ofthe specific mask, and be defined (as part of method 600 or otherwise)based on other considerations—such as the physical characteristicsrepresented by each of the types of segments.

Any stage of method 600 may be carried out by a person (especially usinga computer), by a computer or other machine, and/or by a combinationthereof.

FIG. 9 illustrates a process 600′ for generating reference data that maybe used for classification (e.g. as in method 500), according to anembodiment of the invention. Especially, process 600′ may be used forthe generation of a mask 400 and a template 300.

The stages denoted with an apostrophe (e.g. 610′, 620′, etc.) arepossible implementations of the corresponding stages of method 600 (e.g.stages 610 and 620 respectively).

Referring to stage 640′ of processing the reference image to provide alow resolution template corresponding to at least an area of thereference image, it is noted that it may be implemented in several ways,some of which are illustrated.

Firstly, a high resolution template-parent 300′ is selected from thereference image 800. The selection is denoted 641′. The high resolutiontemplate-parent 300′ may be identical to selected cell-sized pattern ofinterest area 810, or different therefrom. The high resolutiontemplate-parent 300′ is then decimated or otherwise downsampled ormanipulated—e.g. by applying morphological image processing techniquessuch as smoothing or edge enhancement (denoted 642′), to providelower-resolution template 300″ (also denoted “downsampled template” and“decimated template”). This template 300″ may optionally be used “as is”as the template provided (this option denoted 643′).

In another implementation (denoted 644′), the downsampled template 300″is used for selecting from a lower resolution image 100′ (e.g. in theresolution in which the inspection image of method 500 is obtained) anarea 190′ that matches the downsampled template 300″, and using thatarea 190′ as the template 300.

Reverting to system 200 and to method 500 as a whole, it is noted thatutilization of system 200 and/or of method 500 may be implemented toenable ultra-fine pattern-based classification, which in turnfacilitates better control of the defect detection process and of theanalysis thereof.

In accordance with an aspect of the presently disclosed subject matter,there is yet further provided a program storage device readable bymachine, tangibly embodying a program of instructions executable by themachine to perform a method for classifying a potential defectidentified within an inspection image of an inspected object, the methodcomprising the steps of determining an anchor location with respect tothe inspection image, based on a matching of a template and a portion ofthe inspection image, wherein an accuracy of the determining of theanchor location exceeds a resolution of the inspection image; based on amask which defines different segments within an area and on the anchorlocation, determining a distribution of the potential defect withrespect to one or more of the segments; and classifying the potentialdefect based on the distribution.

In accordance with an embodiment of the presently disclosed subjectmatter, there is yet further provided a program storage device, whereinthe inspected object is selected from a group consisting of anelectronic circuit, a wafer, and a photomask.

In accordance with an embodiment of the presently disclosed subjectmatter, there is yet further provided a method, wherein the determiningof the distribution comprises determining the distribution at anaccuracy which exceeds the resolution of the inspection image.

In accordance with an embodiment of the presently disclosed subjectmatter, there is yet further provided a program storage device, whereinthe different segments correspond to parts of the inspected objecthaving different physical characteristics.

In accordance with an embodiment of the presently disclosed subjectmatter, there is yet further provided a program storage device, whereinthe classifying comprises classifying the potential defect according toa classification in which classes correspond to defect types whoseimplications on an operability of the inspected object differ.

In accordance with an embodiment of the presently disclosed subjectmatter, there is yet further provided a program storage device,comprising determining multiple anchor locations with respect to theinspection image, based on matching of the template and multipleportions of the inspection image; wherein accuracies of the determiningof the multiple anchor locations exceed the resolution of the inspectionimage; based on the mask and on the multiple anchor locations,determining distributions of the potential defect with respect to one ormore of the segments; and classifying the potential defect based onmultiple distributions determined for it.

In accordance with an embodiment of the presently disclosed subjectmatter, there is yet further provided a program storage device, furthercomprising selecting for further scanning potential defects which areclassified into certain classes, wherein the selecting includesrefraining from selecting potential defects classified into at least oneclass other than the certain classes; and selectively scanning, in aresolution which is higher than the resolution of the inspection image,at least one area of the inspected object which is selected based onlocations of the potential defects which are selected.

In accordance with an embodiment of the presently disclosed subjectmatter, there is yet further provided a program storage device, whereinthe mask is determined based on a reference image of an inspected-objectreference area, wherein the method further comprises generating thetemplate, wherein the generating comprises downsampling a part of thereference image.

In accordance with an embodiment of the presently disclosed subjectmatter, there is yet further provided a program storage device, whereinthe reference image is generated from computer-aided design (CAD) data.

While certain features of the invention have been illustrated anddescribed herein, many modifications, substitutions, changes, andequivalents will now occur to those of ordinary skill in the art. It is,therefore, to be understood that the appended claims are intended tocover all such modifications and changes as fall within the true spiritof the invention.

It will be appreciated that the embodiments described above are cited byway of example, and various features thereof and combinations of thesefeatures can be varied and modified.

While various embodiments have been shown and described, it will beunderstood that there is no intent to limit the invention by suchdisclosure, but rather, it is intended to cover all modifications andalternate constructions falling within the scope of the invention, asdefined in the appended claims.

What is claimed is:
 1. An analysis system for classifying possibledefects identified within an inspection image of an inspected object,the system comprising: a storage device; and a processor, coupled to thestorage device, to: match a template and a portion of the inspectionimage, thus giving rise to a matching portion of the inspection image,wherein the inspection image is captured by an inspection tool;determine, using a mask corresponding to the template and defining oneor more segments within the matching portion of the inspection image, alocation of a potential defect with respect to the one or more segments,thus giving rise to a matching segment of the inspection image, thematching segment corresponding to the location of the potential defect;and classify the potential defect based on the matching segment, therebyconsidering the location of the potential defect with regard to therespective segment defined within the inspection image, wherein theprocessor is further to define the mask based on a reference image of aninspected-object reference area, to downsample a part of the referenceimage, and to generate the template based on a result of thedownsampling, the reference image and the mask are characterized by aresolution exceeding a resolution of the inspection image.
 2. The systemaccording to claim 1, wherein the inspected object is selected from agroup consisting of an electronic circuit, a wafer, and a photomask. 3.The system according to claim 1, wherein the processor is further todetermine the location of the potential defect with respect to the oneor more segments at an accuracy which exceeds the resolution of theinspection image.
 4. The system according to claim 1, wherein at leasttwo segments among the one or more segments correspond to parts of theinspected object having different physical characteristics.
 5. Thesystem according to claim 1, wherein the processor is to classify thepotential defect according to a classification in which at least twoclasses corresponding to different segments among the one or moresegments characterize defect types whose implications on an operabilityof the inspected object differ.
 6. The system according to claim 1,wherein the processor is further to select for further scanningpotential defects which are classified into certain classes, wherein theselecting includes refraining from selecting potential defectsclassified into at least one class other than the certain classes;wherein the system further comprises an inspection module, configured toselectively scan, in a resolution which is higher than the resolution ofthe inspection image, at least one area of the inspected object which isselected based on locations of selected potential defects.
 7. Acomputerized method for classifying a potential defect identified withinan inspection image of an inspected object, the method comprising:matching a template and a portion of the inspection image, therebydetermining a matching portion of the inspection image, wherein theinspection image is captured by an inspection tool; using a maskcorresponding to the template and defining one or more segments withinthe matching portion of the inspection image for determining a locationof the potential defect with respect to the one or more segments, thusgiving rise to a matching segment of the inspection image, the matchingsegment corresponding to the location of the potential defect; andclassifying the potential defect based on the matching segment, therebyconsidering the location of the potential defect with regard to therespective segment defined within the inspection image, wherein the maskis determined based on a reference image of an inspected-objectreference area, the method further comprises generating the template,wherein the generating comprises downsampling a part of the referenceimage, and wherein the reference image and the mask are characterized bya resolution exceeding a resolution of the inspection image.
 8. Themethod according to claim 7, wherein the inspected object is selectedfrom a group consisting of an electronic circuit, a wafer, and aphotomask.
 9. The method according to claim 7, wherein the determiningof the location of the potential defect with respect to the one or moresegments is provided at an accuracy which exceeds the resolution of theinspection image.
 10. The method according to claim 7, wherein theclassifying comprises classifying the potential defect according to aclassification in which at least two classes corresponding to differentsegments among the one or more segments characterize defect types whoseimplications on an operability of the inspected object differ.
 11. Themethod according to claim 7, wherein the reference image and/or the maskare generated from computer-aided design (CAD) data.
 12. Anon-transitory computer readable storage medium having instructionsthat, when executed by a processor, cause the processor to perform amethod for classifying a potential defect identified within aninspection image of an inspected object, the method comprising: matchinga template and a portion of the inspection image, thereby determining amatching portion of the inspection image, wherein the inspection imageis captured by an inspection tool; using a mask corresponding to thetemplate and defining one or more segments within the matching portionof the inspection image for determining a location of the potentialdefect with respect to the one or more of the segments, thus giving riseto a matching segment of the inspection image, the matching segmentcorresponding to the location of the potential defect; and classifyingthe potential defect based on the matching segment, thereby consideringthe location of the potential defect with regard to the respectivesegment defined within the inspection image, wherein the mask isdetermined based on a reference image of an inspected-object referencearea, and the template is generated using downsampling a part of thereference image, wherein the reference image and the mask arecharacterized by a resolution exceeding a resolution of the inspectedimage.
 13. The non-transitory computer readable storage medium accordingto claim 12, wherein the determining the location of the potentialdefect with respect to the one or more segments comprises determiningthe location at an accuracy which exceeds the resolution of theinspection image.
 14. The non-transitory computer readable storagemedium according to claim 12, wherein the classifying comprisesclassifying the potential defect according to a classification in whichat least two classes corresponding to different segments among the oneor more segments characterize defect types whose implications on anoperability of the inspected object differ.